In a competitive market, it is of great significance to divide customer groups to develop customer-centered personalized products. In this paper, we propose a customer segmentation method based on the K-means algorithm and the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm easily falls into local extremum, the improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to improve optimization accuracy. The full factorial design is used to determine the optimal parameter combination; the roulette operator is used to select excellent particles; then, the selected particles are crossed according to their adaptive crossover probabilities; when the population falls into a local optimum, the particles are mutated according to their adaptive mutation probabilities. Aimed at the K-means’ sensitivity to selecting the initial cluster centers, IHPSO is used to optimize the cluster centers (IHPSO-KM). We compare IHPSO with the PSO, LDWPSO, GA, GA-PSO and ALPSO algorithms on nine benchmark functions. We also conduct comparative experiments to compare IHPSO-KM with several conventional and state-of-the-art approaches on five UCI datasets. All results show that the two proposed methods outperform existing models. Finally, IHPSO-KM is applied in customer segmentation. The experimental results also prove the rationality and applicability of IHPSO-KM for customer segmentation.

The old-age dependency ratio (ODR) is an important indicator reflecting the degree of a regional population’s aging. In the context of aging, this study provides a timely and effective method for predicting the ODR in Chinese cities. Using the provincial ODR from the Seventh National Population Census and Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data, this study aims to predict and analyze the spatial correlation of the municipal ODR in Chinese cities. First, the prediction model of the ODR was established with curve regression. Second, the spatial structure of the municipal ODR was investigated using the Moran’s I method. The experimental results show the following: (1) the correlation between the sum of the nighttime light and ODR is greater than the mean of nighttime light in the study areas; (2) the Sigmoid model fits better than other regression models using the provincial ODR in the past ten years; and (3) there exists an obvious spatial agglomeration and dependence on the municipal ODR. The findings indicate that it is reasonable to use nighttime light data to predict the municipal ODR in large and medium-sized cities. Our approach can provide support for future regional censuses and spatial simulations.